Steel corrosion in reinforced concrete (RC) structures is a major concern in civil engineering, as it directly affects the safety, serviceability, and lifespan of infrastructures. This study focuses on understanding how corrosion-induced deterioration in steel reinforcement impacts the structural performance of concrete beams. In particular, it investigates how different levels of reinforcement loss influence the load-bearing capacity and failure mechanisms of RC beams under combined bending and shear. To achieve this, a numerical approach was adopted using the ABAQUS finite element software. Three beam models were developed, each representing a different degree of corrosion: intact reinforcement, 20% loss, and 25% loss in cross-sectional area. The models were designed to closely simulate the physical behavior of corroded beams, incorporating nonlinear material properties, bond degradation, and failure criteria. The findings reveal a clear decline in structural performance as corrosion severity increases. Beams with higher reinforcement loss exhibited reduced ultimate loads, increased deflections, and altered failure modes - particularly in shear-dominated regions. The simulation results aligned well with available experimental data, demonstrating the accuracy and reliability of the modeling approach. This research highlights the importance of early detection and quantification of corrosion damage in RC structures. By employing advanced finite element tools like ABAQUS, engineers can better predict structural degradation, evaluate safety margins, and plan timely interventions. The study provides valuable insights into infrastructure asset management and supports the development of effective maintenance strategies aimed at extending the service life of aging concrete structures exposed to aggressive environments.

KEYWORDS

Reinforcement, Deflection, Numerical, Analysis, ABAQUS, FE Modelling

Cite This Paper in IEEE or APA Citation Styles

(a). IEEE Format:

[1] Bashir Saleh , "Numerical Evaluation of Corrosion-Induced Degradation in Reinforced Concrete Beams Using Finite Element Analysis in ABAQUS," Civil Engineering and Architecture, Vol. 13, No. 5, pp. 3967 - 3974, 2025. DOI: 10.13189/cea.2025.130536.

(b). APA Format:

Bashir Saleh (2025). Numerical Evaluation of Corrosion-Induced Degradation in Reinforced Concrete Beams Using Finite Element Analysis in ABAQUS. Civil Engineering and Architecture, 13(5), 3967 - 3974. DOI: 10.13189/cea.2025.130536.

This paper presents a comprehensive study of modelling human body blockage (the most critical challenges in fifth-generation (5G)) effects on indoor millimetre wave (mmWave) communication links at 32.5 GHz, a key frequency for 5G networks. Through controlled experiments in a laboratory environment, we analyse signal attenuation as a human subject obstructs the line-of-sight (LOS) path between transmitter and receiver, recording received power at incremental positions. To model the observed phenomena, we propose a hybrid framework integrating deterministic and statistical components: (1) a modified Double Knife-Edge Diffraction (DKED) model with Gaussian-shaped blockage attenuation (20.8 dB peak at full blockage) and reflection-induced signal enhancement (−15.0 dB peak from nearby objects), and (2) a log-normal shadowing component (σ = 11.8 dB) capturing environmental randomness. Our results reveal strong agreement between simulations and measurements, achieving a mean absolute error of 3.2 dB and a correlation coefficient R² = 0.89. The analysis demonstrates that human-induced diffraction dominates near the LOS centre, while multipath reflections significantly alter signal strength at peripheral positions. We further derive practical guidelines for 5G network design, recommending a 44.4 dB link budget safety margin to account for combined blockage and shadowing effects. This work advances indoor mmWaves channel modelling by unifying physics-based diffraction analysis with empirical reflection characterization, the framework achieves strong experimental validation and offers actionable insights for 5G network design

The advent of 5G networks has revolutionized wireless communications by unlocking unprecedented data rates through millimeter-wave (mmWave) frequencies. However, the short wavelengths of mmWave signals (e.g., 32.5 GHz) make them highly vulnerable to obstructions, particularly human blockage, posing significant challenges for reliable link prediction and network planning. Existing models often oversimplify human-induced attenuation, limiting their accuracy in real-world scenarios. This work addresses this gap by proposing a cylindrical diffraction model to quantify human blockage effects at 32.5 GHz—the first application of such a model at this frequency. Through controlled experiments, we measured signal degradation as a human subject progressively blocked a 2-meter mmWave link, revealing a sharp decline in received power from −41.2 dBm (no blockage) to −69.7 dBm (full blockage). The cylindrical model demonstrated strong alignment with empirical trends, accurately capturing the nonlinear increase in attenuation as the human approached the line-of sight path. Notably, the model matched baseline measurements within 1.4 dB and predicted full-blockage loss within 7 dB of observed values, despite inherent simplifications. This study underscores the efficacy of cylindrical modelling for mmWave blockage prediction while highlighting critical refinements needed for practical deployment, such as incorporating material properties and antenna radiation patterns. By bridging theoretical and empirical insights, our work provides a foundational framework for enhancing 5G/6G network resilience in human-dense environments, ensuring robust performance for high-data-rate applications. 

ZnO thin films were deposited on stainless steel substrates using the immersion method by immersing the

substrates in a sol-gel coating solution at temperatures of 70 °C and 80 °C for varying durations of (1, 2, 3, and 4minutes).

The results indicated that increasing the immersion time significantly influenced the film thickness. Optical measurements

showed that transmittance at a wavelength of 350nm increased with higher deposition temperatures. Additionally, the

Urbach tail energy increased with temperature, whereas the band gap (Eg) decreased markedly. Furthermore, the

electrical conductivity of the ZnO films improved with increased temperature and immersion time.